This paper is generally well written on assessing the performance of SWOT measurement of surface water equivalent (WSE) over a narrow river during a flood event.  The study first compared SWOT measurement with a tide gauge and obtained good results, suggesting SWOT is meeting mission requirement even for rivers with width less than 50 m.  SWOT data over the studied river were then compared with model simulations.  The close agreement of the comparison has validated the model and demonstrated the utility of SWOT in flood monitoring and modeling.

I recommend the paper be published with the following comments for the authors to consider for a revision.

Lines 74-82:  The discussions on the performance of SWOT from various sources need to be summarized in a coherent manner.  There are three scales involved: a global assessment of 0.15 m  (Yu et al,2024); a regional assessment of 0.25 m (Jiang et al 2025), and a subcontinental (over India) assessment of 18 cm (Patidar et al 2025).  The latter two are for narrow rivers, but the first seems to be for all rivers.  I think these results need to be discussed in the context of spatial scales versus the mission requirement.

Lines 219-223:  The discussion here is very confusing. On Fig 4,  I don’t find the information on probability,  the 68^th percentile is 0.05 m, RMSE 0f 0.24 m?  Something is missing on Fig 4, which shows only the comparison of WSE from SWOT with model simulations.

Lastly, I would suggest that all the figure captions be more elaborate with some details.

The manuscript is based on an important and relevant problem by assessing the applicability of SWOT-derived water surface elevations (WSE) for flood monitoring (during the calibration orbit) and hydraulic modeling in rivers narrower than the nominal SWOT detection threshold (< 50 m). The use of an actual extreme flood, rather than simulated SWOT data, is a good approach. Overall, the paper is technically detailed, well referenced, and well written.

However, while the study is a good case study, its scientific novelty and strength of conclusions remain limited. The manuscript demonstrates consistency between SWOT WSE and a pre-existing LISFLOOD-FP model, rather than providing a rigorous, independent assessment of SWOT performance in narrow rivers. As a result, contribution is not significant and shows simply as case study rather than advancing understanding beyond what has already been shown in recent studies, especially for the narrow river (<50 m).

The specific comments are as given below:

1. The main conclusion of this study that SWOT WSEs are “sufficiently accurate” for a ~40 m wide river, is already well reported in the recent past studies comparable or better performance for rivers < 50–100 m. However, this manuscript failed to justify what new insight is added beyond confirming prior findings at one site. I suggest authors should critically investigate (1) whether measurements errors are systematic or site-specific, (2) whether performance is robust to geometry, slope, or hydraulic complexity, or (3) whether SWOT observations add information beyond what the hydraulic modelling framework (LISFLOOD-FP) already assumes.
2. It looks like the manuscript is the lack of independence between the SWOT observations and data and LISFLOOD-FP model used for evaluation. The LISFLOOD-FP model is based on LiDAR-derived bathymetry and unchanging roughness assumptions, and discharge inputs that are uncertain during the flood peak, and most important issue this model is not calibrated. However, SWOT WSEs are then compared against this model and refereed “accurate” whenever good agreement is found. Additionally, during the flood peak (cycle 508), SWOT is explicitly used to calibrate tributary discharge, after which agreement improves. This creates issue, as SWOT validates a model that is partially constrained or adjusted using SWOT itself. Consequently, the reported RMSE and bias values cannot be interpreted as true SWOT measurement errors.
3. The absence of independent, spatially distributed in situ water level data along the studied river reach further lacks sufficient validation. Apart from the downstream tide gauge, no ground-based WSE measurements are available during the flood event, especially along the main channel where SWOT nodes are evaluated. As a result, the performance of SWOT upstream is referred entirely from agreement with the hydraulic model rather than from direct observations. While drone imagery provides useful qualitative confirmation of flood extent, it does not offer independent vertical validation of WSE. This limitation is referred only briefly but should be emphasized more strongly, as it fundamentally constrains the strength of the conclusions regarding SWOT accuracy in narrow rivers.
4. The discharge calibration for the Des Mares tributary during the flood peak is presented as a key demonstration of SWOT’s advantage, but I feel it is not sufficiently justified/analyzed to support these strong claims. Authors presented a single calibrated scenario and fail to provide any sensitivity analysis to demonstrate that the improved agreement is exclusively attributable to the adjusted tributary discharge. Other factors, such as roughness values, downstream boundary conditions, or bathymetric uncertainty, could possibly produce similar improvements. As such, the conclusion that SWOT observations reveal discharge underestimation and effectively constrain ungauged tributary inflows is reasonable but not conclusively demonstrated.
5. Authors also failed to provide a critical discussion on interpretation of error metrics. In this manuscript RMSE values up to 0.54 m and biases as large as ±0.44 m is described as good agreement, yet these errors are significant relative to flood-stage variations and operational flood-monitoring requirements. Moreover, the manuscript also fails to justify whether these errors are driven primarily by SWOT measurement uncertainty or by hydraulic model limitations. Authors should explore where and why SWOT performs good or poor in a narrow channel, maybe they can explore spatial patterns in error related to channel geometry, curvature, or node averaging effects in a ~40 m wide channel.
6. Increase the font size of Figures axis, and legends for ease of readability (e.g., Figure 4, Fig.5).

The paper is well written and easy to follow. The use of SWOT calibration-orbit data during a real flood event is a valuable contribution. The comparison with tide gauge data shows good quality of SWOT WSE in this area. Testing SWOT during different flood phases (before, during, and after the peak) is also a strength of this study. However, the study covers only one river and one flood event, and most comparisons are with the hydraulic model rather than independent observations along the reach. I suggest the authors be more careful about broad claims on SWOT performance in rivers <50 m.

[Lines 27–28, 271–273] The abstract and discussion suggest the results apply broadly to narrow rivers, but the study only covers one ~40 m river and one flood. I suggest the authors clearly state that these findings are specific to this site and event.

[Lines 238–243, 290–295] For cycle 508, the authors adjusted the model discharge using SWOT data and then compared the model back to SWOT. I have two concerns: (a) please report bias and RMSE both before and after the adjustment, and (b) please clarify that this comparison shows model-SWOT consistency, not independent validation of SWOT accuracy.

[Lines 23–25, 271–273] The authors describe bias up to 0.44 m and RMSE up to 0.54 m as "good agreement" and "satisfactory accuracy," but do not justify why. Whether these errors are acceptable depends on the WSE amplitude during the flood. I suggest the authors discuss the error magnitude relative to the flood signal (for example, RMSE as a percentage of WSE range) to support these claims.

[Lines 195–198] Some nodes were removed manually, but the criteria are not clear. I suggest the authors (a) define objective rules for removal, (b) report how many nodes were removed per cycle, and (c) test how sensitive the results are to this step.

[Figure 5] I find it hard to tell the two groups apart. I suggest using different line styles or separate panels.